The present invention relates generally to the field of signal processing. More specifically, the present invention relates to signal processing a characteristic signal of a subject.
In industrial automation, signal processing is used to classify an object being manufactured or processed based on a characteristic of the object. For example, an apple might be classified by a weight sensor configured to sense the weight of the apple. If the weight is greater than a predetermined weight, the apple is identified as xe2x80x9cgoodxe2x80x9d, and, if not, the apple is identified as xe2x80x9cbadxe2x80x9d.
However, the object can also be classified by other signals. For example, the apple might also be classified by acquiring a color digital image of the apple. If the apple is darker than a predetermined gray scale, or if the apple lacks sufficient red color, the apple is identified as xe2x80x9cbadxe2x80x9d. The challenge is to determine which characteristic (e.g., weight, color, gray scale, etc.) best classifies the objects into the desired classifications, so that the best characteristic can be used during production to automatically classify objects.
A standard method for evaluating the classification of objects has been implemented which assumes a bimodal distribution of the measured characteristic, the distributions assumed to be Gaussian. For example, referring to FIG. 1, this standard method generates a histogram 9 of the frequency of occurrence of different values of the characteristic. The x-axis represents the values of the characteristic (e.g., weight, color, etc.) and the y-axis represents the frequency of objects having that characteristic. A first mode 11 includes objects in a first class (e.g., xe2x80x9cbadxe2x80x9d objects) and a second mode 13 includes objects in a second class (e.g., xe2x80x9cgoodxe2x80x9d objects). According to this method, the mean values 17, 15 of each mode are identified, the variances of mean values 17, 15 are determined, and the distance 19 between mean values 17 and 15 is determined. The smaller the variances and the greater the interval between mean values 17, 15, the greater is the quality of the characteristic for classification of this object.
One drawback of this method is that characteristic distributions frequently are neither bimodal nor Gaussian and, thus, are incorrectly evaluated by this prior method. With reference to FIG. 2, a frequency distribution 21 of another characteristic is shown, in which mode 23 is not Gaussian. Further, mode 23 includes objects in a first class, mode 24 includes objects in a second class, and mode 26 includes additional objects in the first class. An example of such a distribution might be one in which the characteristic is the length of a wooden dowel, wherein xe2x80x9cgoodxe2x80x9d dowels must have a length within a certain tolerance. Thus, xe2x80x9cbadxe2x80x9d dowels have lengths greater than (mode 26) and less than (mode 23) xe2x80x9cgoodxe2x80x9d dowels (mode 24). Prior methods will not adequately evaluate the suitability of this characteristic for classification purposes, since the distribution in FIG. 2 is not Gaussian and not bimodal.
Accordingly, there is a need for a system and method for evaluating the suitability of characteristics for classification. There is further a need for such a system and method which is applicable to non-Gaussian distributions. Further still, there is a need for such a system and method which is applicable to non-bimodal distributions. There is also a need for such a system and method which is robust against noise.
According to an exemplary embodiment, a method of evaluating a characteristic for suitability in classification of subjects based on subject data is provided. The subject data includes characteristic data and class data. The method includes arranging the subject data based on the characteristic data, and identifying the number of class changes from one class to another class in the arranged subject data. The number of class changes represents the suitability of the characteristic for classification of the subjects.
According to an alternative embodiment, a method of evaluating a characteristic for suitability in classification of subjects based on subject data is provided. The subject data includes characteristic data and class data. The method includes arranging the subject data based on the characteristic data, identifying consecutive subject data having a class change, and measuring the interval between the two consecutive subject data. The interval between class changes represents the suitability of the characteristic for classification of the subject.
According to yet another alternative embodiment, a system for evaluating a characteristic for suitability in classification of subjects is provided. The system includes sensing means for acquiring characteristic data from a plurality of subjects and classification means for classifying each subject with one of a first class and a second class. The system further includes means for arranging the subject data based on the characteristic data and identifying the number of class changes from one class to another class in the arranged subject data. The number of class changes represents the suitability of the characteristic for classification of the subjects.